Based on The Practice of Statistics, 4th edition teacher resources. Some questions were adjusted/modified.
Select the best answer
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Question 1
1.
You have data for many families on the parents’ income and the years of education their eldest child completes. Your initial examination of the data indicates that children from wealthier families tend to go to school for longer. When you make a scatterplot,
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Question 2
2.
The correlation between the heights of fathers and the heights of their (fully grown) sons is r = 0.52. This value was based on both variables being measured in inches. If fathers' heights were measured in feet (one foot equals 12 inches), and sons' heights were measured in furlongs (one furlong equals 7920 inches), the correlation between heights of fathers and heights of sons would be
For children between the ages of 18 months and 29 months, there is an approximately linear relationship between height and age. The relationship can be represented by yˆ = 64.93 + 0.63x, where y represents height (in centimeters) and x represents age (in months).
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Question 3
3.
Lorena is 22.5 months old. What is her predicted height?
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Question 4
4.
Jonathan is 20 months old and is 75 centimeters tall. What is his residual?
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Question 5
5.
Which of the following statements is/are true?
I. Correlation and regression require that there are clearly-identified explanatory and response variables.
II. Scatterplots require that both variables be quantitative.
III. Every least-squares regression line passes through
A study of the fuel economy for various automobiles plotted the fuel consumption (in liters of gasoline used per 100 kilometers traveled) vs. speed (in kilometers per hour). A least squares line was fit to the data. Here is the residual plot from this least-squares fit.
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Question 6
6.
What does the residual plot tell you about the linear model?
Leonardo da Vinci, the renowned painter, speculated that an ideal human would have an armspan (distance from the outstretched fingertip of the left hand to the outstretched fingertip of the right hand) that was equal to his height. The following computer regression printout shows the results of a least-squares regression of armspan on height, both in inches, for a sample of 18 high school students.
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Question 7
7.
Which of the following statements is false?
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Question 8
8.
A study found correlation r=0.61 between the sex of a worker and his or her income. From this information, we can conclude that...
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Question 9
9.
Which of the following statements about the correlation r are true?
I. The correlation and the slope of the regression line have the same sign
II. A correlation of -0.35 and a correlation of 0.35 show the same degree of association about the regression line
III. A correlation of 0.75 indicates a relationship is 3 times as linear as one for which the correlation is only 0.25
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Question 10
10.
A copy machine delader has data on the number x of copy machines at each of 89 customer locations and the number y of service calls in a month at each location. Summary calculations give the data below. What is the y intercept of the least-squares regression line of number of service calls on number of copiers?
Part 2: Free Response
Show all your work. Indicate clearly the methods you use, because you will be graded on the correctness of your methods as well as on the accuracy and completeness of your results and explanations.
How are traffic delays related to the number of cars on the road? Below is data on the total number of hours of delay per year at 10 major highway intersections in the western United States versus traffic volume (measured by average number of vehicles per day that pass through the intersection).
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Question 11
11.
Describe what the scatterplot reveals about the relationship between traffic delays and number of cars on the road.
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Question 12
12.
Suppose another data point at (200000, 24000), that is 200,000 vehicles per day and 24,000
hours of delay per year, were added to the plot. What effect, if any, will this new point have
on the correlation between these two variables? Explain.
Below is computer output for the regression of hours of delay versus number of vehicle per day.
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Question 13
13.
What is the slope of the regression line? Interpret the slope in the context of this problem.
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Question 14
14.
Explain what the quantity S = 3899.57 measures in the context of this problem.